by Carol Vercellino, CEO of Oak City Labs


Machine learning is one of the most significant technological advancements in recent history. It’s experiencing incredible growth, and innovators are making breakthroughs in ways that no one ever could have expected. 

Because of this, machine learning has changed and will continue to change a lot over the coming years. You’re probably familiar with the buzzword, but the fact remains that many people can’t really answer the question of what machine learning is – and what it isn’t.

In this article (and video), we’ll talk about what machine learning really means, how it’s changed over the years, and where we think machine learning will go next.

What is machine learning?

Machine learning is a process where you use algorithms to parse data, learn from it, and make a prediction.

For example, one of the most common ways people are using machine learning right now is with stock market predictions. You can try to predict what a particular stock is going to do by combining historical stock data with other data, like weather, GDP, and even election data.

On a more personal note, if you have an iPhone, you can label your pet, child, partner, or friend in one of your photos. You’ll then notice that Apple Photos goes back through your album and labels any other pictures of that person or animal. You can also use search terms like ‘cake’ or ‘beach’ to sort through photos. That’s machine learning on the back end.

Machine learning is not where you have a whole bunch of data that people – or statisticians – manually go through (to learn more about the differences between machine learning and statistics, check out this article).

It’s an automated process, and the data is like a black box to the machine. The machine takes that data, dumps it into different models, and tries to develop the best possible answer. So, the machine doesn’t care whether it’s exploring weather or GDP data. 

A brief history of machine learning:

How does machine learning work?

Within machine learning, there are 3 different types of learning:

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

Supervised Learning

Supervised learning is when you give labeled data to the machine. For example, you have a whole data set full of cats and dogs. You give the machine that data set, and the machine learns what a cat is versus a dog. Then, you give the machine a photo that hasn’t been labeled and ask the machine to identify it as a cat or dog. And the machine can do that using algorithms and a little bit of human intervention.

Unsupervised Learning

Unsupervised learning is when the data is unlabeled. Take the same example, but instead of giving the machine images labeled as cats and dogs, you leave the photos unlabeled and ask the machine to look for patterns in those images. Then, you can come up with a label for those patterns. 

Reinforcement Learning

Reinforcement learning is much like playing a video game. In a video game, the user goes through a level and gets a reward or badge before proceeding to the next level. Reinforcement learning is very much like that. The automation or algorithms attempt to find the most optimal way to accomplish a task. 

When the machine does well, it moves onto the next task, and it continues to try and get better and better each time. 

What’s next in machine learning?

Machine learning is becoming more commonplace, but we’re also moving towards getting more information around the data. 

According to Jason Burke of CREO, Inc., the biggest problem in the machine learning space right now is not the algorithms or math; it’s cleaning the data and the context of the data. We need to combine statistics and machine learning to get more context around the data itself. 

We believe we’ll see advancements in that area in the coming years, plus more user-friendly platforms where anybody can sit down and use data science and machine learning.

Machine learning will undoubtedly continue to disrupt every industry. How are you going to use machine learning to impact your company, industry, or the world?

Speaking of advancements in technology…here’s what you need to know about Apple’s new iPads, watches, and the iOS 14 release. 

89% of healthcare organizations experienced a data breach in the past two years. And the Achilles heel seems to be us humans (we do have a tendency to err.) Take a look at the numbers to see what challenges healthcare companies are facing. 

 

Cybersecurity in Healthcare: The Human Weakness

Using an iPad for work, app, and social needs

By: Carol Vercellino, CEO & Co-Founder and Jay Lyerly, CTO & Co-Founder

Last week’s news was dominated by Apple’s event and the release of iOS 14 – a huge surprise to developers like us. If you missed it or want a quick break down of the new Apple Watch and iPad features, watch our update below. We also talk about the opportunities for health tech innovation around the new blood oxygen and sleep monitoring systems. Plus, what you need to know about iOS if you’re releasing an app.

Carol Vercellino: What do you think about Apple’s announcement last week, and what are you most excited about?

Jay Lyerly:  So, Apple announced a number of things. A lot of people were expecting iPhones, but that’s going to be next month, so they’re just focusing on the Apple Watch and iPad on this one, plus a few extra little surprises.

The big update to the Apple Watch Series 6 is the blood oxygen monitoring sensor – you can manually take a reading with an ECG from previous models, and it also works in the background, monitoring your o2 periodically. Other than that, the new watch also has a faster CPU and is more responsive.

Apple also came out with the Apple Watch SE model, which is an entry-level watch. It’s really interesting because I think Apple feels like the watch has reached a critical level of functionality, and so they’ve locked that in with SE. It’s got cellular servers, GPS, and more, and now that’s good for lots of people. And then you have the advanced line for people who want more cutting-edge features – like the o2 sensor.

As far as the o2 sensor, I think that’s really interesting. They didn’t really talk about the applications of that a whole lot. In the context of COVID, I read that one of the things COVID does is suppress oxygen. So, some people have come in with what they call silent hypoxia, where they have COVID, but they don’t have any of the other symptoms. But it’s suppressed their oxygen levels in their blood, so it’s at a critical level where they’re starting to feel lethargic, and it gets to the point where it starts to affect organs.

One of the things the watch does with the background o2 sensor readings is it can warn you if your o2 level goes down.

The other interesting feature of the o2 sensor is sleep apnea. The watch now has built-in sleep monitoring. They suggest you sleep with your watch on and record that data. With the o2 sensors, that can indicate sleep apnea. It’ll be interesting to see how that gets used in a clinical sense.

CV: As a parent, I’m most excited about the family set up. And I’m wondering if there will be any applications for families to monitor medical information for the future. Do they have that now?

JL:  I’m not sure how the family is integrated with the health kit features, but they’re certainly pushing it with both children and older adults – or people in your family who might not have an iPhone. I think it’s especially useful with fall monitoring. 

CV: Is fall monitoring on by default?

JL:  If you’re under 55, you have to turn that on manually.

CV: What are you most excited about with the iPad?

JL:  They bumped two of the lines. There are basically three lines – the iPad, the iPad Air, and the iPad Pro. This past week, they didn’t update the iPad Pro as that was updated in the spring. 

With the low end, they bumped it to a new, faster processor and included the neural engine for the first time. That means a lot of the machine learning they’ve been working on can run on that natively now, so it’ll be a lot faster. That’s interesting in that it means that the base machine is still going to have a really long life span. If you’re looking to deploy a healthcare app on that, you can get the low-end machine, but it can still perform really well and do a lot of that machine learning analysis on board, and still have a cellular connection if you’d like. 

The iPad Air update was pretty big. They did a big redesign and a lot of the features from the pro line trickled down to that. In my mind, that’s really the workhorse. If you don’t have a whole Mac that you use every day, and you want an iPad to replace that desktop, the Air is where you want to start. It’s got pretty much all the features now, and it’s in that mid-range price tier. 

They updated the chip in the Air – it’s really fast. And they added Touch ID in the power button, which is interesting in the context of the iPhone coming out next month.

With the iPhone coming out next month, it’s expected to be on that same A14 chip, and they didn’t really talk about its performance at the event, other than to say it’s faster than the old iPads. So, they seem to be waiting on the big reveal next month to talk more about its capabilities.

CV: iOS 14 came out, and there was a little bit of a surprise that it came out so quickly.

JL: It was a surprise! Tim Cook was on stage and said, ‘We’ll be releasing this tomorrow!’ And all the developers were expecting to have at least a couple of days – usually, we get a week’s notice.

When you deploy a new app on a new OS like that, you have to use the release version of X code, which they don’t release until that announcement is made, so basically all the developers have to download X code, get that installed, recompile their apps, do testing, and ship it off through App Review if they want to be there for the launch date. So, a lot of people were surprised and had a long night ahead of them.

CV: Is there anything in iOS 14 that people need to be aware of for their app?

JL: I think one of the things – out of the gate – that has been really popular has been the app widgets. It’s been in Android for a while, but you can do basically mini applets on the home screen, where you would normally launch apps, and provide content from your app right there.

For example, if you have a weather app, you might put a map or forecast there. Or, the fitness app now has your rings on there, so you can see that updated on your phone in real-time. That’s gotten a lot of press because people have been waiting for a long time.

The other thing that is coming is called App Clips. These are really scaled-down, mini-applications that get installed on the fly. So, it’s the kind of thing where you can go to a parking lot and scan a QR code and get a mini parking lot app to pay for your parking space. 

Services like parking spaces, renting a scooter, going into a restaurant, and ordering food, that takes a lot more developer work, so that’s still probably something we’ll see ramp up over the next couple of months.

**The above interview has been transcribed for clarity and brevity.**

 

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Get started today with data management in your startup

by Carol Vercellino, CEO of Oak City Labs


To thrive in a customer-centric world, businesses need to know not just how to collect data but how to manage it. Data management is your startup’s practice of collecting, keeping, and using data. 

When managed well, you can use data to tailor your content, products, and services to your audience and gain a massive competitive advantage.

When we consult our software development clients on their data management practices, we like to focus on three main areas as we help them build their data management program:

  • Data Integrity
  • Data Accessibility 
  • Data Security

Data Management for Startups

Step 1: Protect Your Data’s Integrity

Data integrity is the accuracy, completeness, and reliability of data throughout its lifecycle. The  integrity of your data is critical in regards to regulatory compliance, security, and your company’s reputation.

There’s an assortment of factors that could affect the integrity of your data, but a few examples include: human error, transfer errors – or when data is transferred from one database to another – bugs and viruses, and compromised hardware.

You can reduce or eliminate the risks to your data’s integrity by doing the following:

  • Backing up your data
  • Validating your data when it’s gathered and when it’s used
  • Logging when data has been added, modified, or deleted
  • Conducting regular, internal audits
  • Using error detection software
  • And, most importantly, creating a single source of truth

A single source of truth is the process of storing and structuring all your data in one master location for editing, referencing, and analyzing. Many businesses, especially those who have been around for 20-30 years, have data scattered everywhere. Aggregating your data in one central location can help you maintain data integrity and optimize it to better your business.

Step 2: Choose Who Has Access to Your Data

Accessibility also plays an important role in data management and integrity. You want to create an environment where data is tightly protected but flexibly used. So, as you develop your infrastructure and select tools for data storage and visualization, consider these 3 questions:

• Where is your data stored?
• Who needs access to your data?
• And how do you want to use it?

Choose a data warehouse or tool that can give you flexible options for permitting or restricting access.

We recommend limiting full access to your data to only the people who need to add, delete, or modify the information.

You can use data visualization tools, like Tableau or Microsoft Power BI,  for team members who only need to view specific data sets in order to improve customer experiences or operational procedures. This also eliminates the frustration of staring at an Excel spreadsheet with 60,000 rows.

Step 3: Secure Your Data

Finally, with data management, you want to make sure all your data is locked up. This goes for every company – no matter how much or what kind of data you collect.

For example, let’s say you’re in a market that has a consumer-oriented app, and you have email addresses for your users. If you’re like most people, you don’t often think about securing email addresses like you might secure private health information in a health or wellness app.

But email addresses are something hackers want.  So, you want to make sure all your data – even small bits of personal information like an email address – is protected from cyber-attacks.

Focusing on your data integrity and accessibility contributes to your data security practices. But, you’ll also want to create documentation that details your data management process and implement company-wide training to instill data security as a priority in your company’s culture.

You can use this level of security as a competitive advantage for your customers. Let your customers know what steps you take to secure their information.

Treat Data as a Valuable Resource

With data management, your goal is to treat data as if it’s a valuable resource and focus on bringing all your data together to make better decisions for your customers and business. 

Focus on these three high-level points – Integrity, Accessibility, and Security – as you develop your product or project, and you can build customer relationships and drive long-term revenue growth.

Want to learn more about how to secure your data in the cloud? Read our article, Is Your Data ‘Really’ Secure in the Cloud?

Women looking over computer data to enhance the customer experience

By: Carol Vercellino, CEO & Co-Founder

 

Even though businesses and schools are opening up, the rise of COVID-19 has brought on the beginning of a new era of digital engagement. Retail, healthcare, and even how you experience a museum has changed forever. And as such, the digital experience businesses offer their customers is more important than ever.

Our CEO and Co-Founder, Carol Vercellino hosted a Q&A with Madison Morgan, Director of Accounts at Well Refined Marketing Agency.  

Watch the video below or read through our transcription to learn how you can use data to enhance your customer experience strategy to be empathetic, authentic, and trustworthy in this new era.

Oak City Labs: Tell me more about Well Refined and what you do.

Madison Morgan: Well Refined is a marketing agency based out of Raleigh and New York. We’re a team of creatives and strategists that help you solve your marketing challenges, so you can think big and act courageously. We like to take all of the marketing strategies in-house and partner with you to take on all of your challenges and help you find the perfect solutions.

I’m the Director of Accounts, so I oversee all of our clients and make sure everyone is well taken care of, and I oversee all of our marketing strategies.

OCL: Why do you think the customer experience matters?

MM:  Your customer experience is really what sets you apart from your competitors. It’s what allows you to make a relationship with your target audience and keep them coming back to you – and not to other competitors in your industry.

As you build those relationships, you’re eventually converting these targeted people from leads into paying customers. And then they’ll become your fans and share your company with their friends, increasing your audience over time. 

OCL: 90% of millennials state that brand authenticity is important. How can brands create authenticity online?

MM: That’s a really great question. My biggest piece of advice is to be who you are. But that doesn’t mean ‘Be Carol as Oak City Labs’; it’s more ‘Be Oak City Labs’. 

So, create your brand identity, figure out what your tone is, what language you use to speak to your audience, and firm up that brand identity, and then stick to it across all platforms. No matter where your audience is meeting you – whether on social media or on your website – you want them to have that same experience with your brand across all platforms.

That’s going to not only create authenticity but also continue growing that relationship with your audience.

OCL: Why is data important when it comes to driving the customer experience?

MM: Data helps you understand your target audience better. And when used correctly, it helps you further strengthen that relationship we’ve been talking about. It builds even more authenticity and helps you make more informed decisions about what marketing direction you want to move towards.

It helps you see what’s working with what you’re doing, what’s resonating with your audience or what’s falling flat, and it ultimately should inform all of your marketing decisions and efforts.

OCL: What are some ways businesses can start to gather data on their customers?

MM: One key, big picture way that’s really easy to do is to survey your audience. Ask them what they want to see, what they want to purchase from you, and even their demographic information can help you speak to them better.

Email marketing is really important, so simply customizing your newsletters or welcome emails to include their first names. Or if you could tag products of interests by including that in the subject line or within the email, it helps build and strengthen the relationship. Ultimately, it can also help you create sales and nurture funnels to speak directly to those interests your audience has indicated.

You can also look at website traffic to see what pages are working well and what’s not. Or taking a step back and looking at general monthly analytics across all platforms. Where are you seeing the highest interactions and where are you not?

OCL: What are some of your favorite tools and approaches for collecting or analyzing data?

MM: For monthly analytics, it can be as simple as a Google Spreadsheet that you type in month-after-month what social media posts are getting the most interaction. Or what emails are getting the highest open rates. Or you can go a little more complex and use something like HubSpot or Sprout. Those are a little bit more pricey and not DIY, so there are pros and cons to both options.

For the surveys, I like using Google Forms because it’s free and you can use it on your own. You can also use paid options like SurveyMonkey.

My biggest recommendation is to use ActiveCampaign. It’s an email marketing platform that’s robust but also very simple for users who are just breaking into the industry. It has options that let you personalize your customer experience.

OCL: Once you have the data you need, what are 3-4 steps someone can take with their data to improve the customer experience?

MM: It’s going to sound really simple, but my four steps are to first, read your data and decide where you want to focus – just pick one target area to look at it. Then, collaborate with your team to see how you can strengthen that area or what you need to do to improve your interactions with customers. Third, create a plan of action with tangible next steps. And finally,  revisit the data and pick another target area of interest to work on next.

OCL: How long should a business gather data before they start analyzing and using it?

MM: Checking in monthly is really good. I don’t think one month of data is enough to make any reasonable decision, but you do want to start looking at those trends month over month. After about four to six months, you can start making decisions and analyzing what’s working well and what’s not in a very informed way.

**The above interview has been transcribed for clarity and brevity.**

 

Enjoy this Q&A? Check out our Q&A with Josh Wyatt, the Chief Information Security Officer with InfiniaML, to learn how to migrate your data to the cloud safely.

3D Printing has the potential to create a post-COVID-19 agriculture world that’s more efficient, less wasteful, and lowers cost for farmers. Check out the possibilities below.

Stuart Bradley is the founder and CEO of not one, but two companies – Carolina Speech Pathology LLC and Altaravision. We caught up with him on a busy Monday afternoon in between meetings, and he was gracious enough to take some time to talk with us about his experience as founder of Altaravision and the interesting journey of their flagship product, NDŌʜᴅ.

Put simply, NDŌʜᴅ is the most portable, high-definition endoscopic imaging system on the market today and an invaluable tool for speech pathologists. It has been extremely well received by the medical community, but its path from concept to market was not without its obstacles.

Where did the idea for NDŌʜᴅ come from? Because it is a very specific product.

It came from a need. Specifically, the need to be able to do machine vision on a Macintosh. Surprisingly, there really wasn’t any software that addressed it anywhere in the marketplace.

Would you mind just briefly explaining what machine vision is?

Sure. Machine vision is the ability for a computer to view imagery or an object, take that information and then display it. Essentially, it is a computer’s ability to see.

And the capacity to do that wasn’t on a Mac? That’s interesting.

Well, no. There was plenty of software out there, but it was all secondary purpose. The bigger issue was that nothing had the capabilities you would need in a medical setting.

It all comes down to video capture. All of the off-the-shelf software could capture images, but they suffered from significant lag. What you saw on the screen might be a full second behind what was happening in real time. That might not seem like much, but when you are dealing with medical procedures that kind of lag isn’t going to cut it.

I played around with off-the-shelf software for a number of years and finally found something I thought might work, but there were a ton of features that I didn’t want or need. I reached out to the developer to make me a one-off, but he was ultimately unable to deliver a final product. That’s what led me to Oak City Labs.

Once you had your software developer in Oak City Labs, what was the hardest part about going from this idea you had to an actual finished product?

By far, the biggest hurdle was doing it in a way that maintains compliance with FDA regulations. Jay Lyerly, the one who was doing the coding, knew that from the start and was able to work with my FDA consultant in a way that we could survive an FDA audit.

The thing is, FDA audits are worse than IRS audits and you’re guaranteed to get one, whereas IRS audits are random. As a medical device company, we are audited every two years by the FDA. Thanks to Jay and Carol at OCL, we’ve been able to pass every single audit with zero deficiencies, which is nearly unheard of.

Was there a moment when you got NDŌʜᴅ out into the world and thought “ok, we did it.”

Yea, there was. With FDA-regulated software you actually do have to draw that line in the sand. Unlike other software development cycles, where updates are always being pushed out, you can’t do that with medical devices. It has to be the finished product from the day it comes out. If you add features, it has to go back through the FDA approval process, which, as you can imagine, is pretty lengthy.

If you could do it all over again, is there anything that you’d do differently?

We bootstrapped the entire thing, with CNP essentially acting like an angel investor for the product. That was really tough, especially when there are people out there actively looking for good products to invest in. If I had to do it again, I would have taken the time to seek out some outside investment instead of just jumping in and doing it all myself.

When you think about where you are today as a business owner, is there anything that sticks out to you as the thing you are most proud of?

Honestly, being able to take on, create, sell and make an actual viable business out of a medical device when I had no prior experience in that industry. I had owned Carolina Speech Pathology for years, but the journey with Altaravision and NDŌʜᴅ was an entirely new one.

What’s your favorite part about doing what you do?

It has to be the satisfaction I get from solving hard problems, and the fact that it’s never boring.

Finally, whenever you have clients or colleagues that are talking about Altaravision or the NDŌʜᴅ product, what do you want them to say or know about it?

I want them to know two things. First, I want them to know it works, and always works. Second, that it is designed to be incredibly easy to use. If you can use Facebook, you can use NDŌʜᴅ.

For more on Oak City Lab’s work with Stuart Bradley and Altavision, check out this article Jay wrote on Computer Vision for Medical Devices via Core Image. If you have an idea and need a software development partner, or if you just have some questions about the development process, we’d love to talk to you. Follow the link below to contact us!

A while back, we introduced you to Amazon Web Services (AWS) for non-technical folks and today we’re continuing the discussion with the AWS cloud based Relational Database Service (RDS). Understanding the basic components of AWS like EC2 and RDS are some of the foundation blocks for most software, including mobile applications. Gaining high level knowledge in these areas can help, whether you’re a new engineer, manager or startup founder. And today’s topic is super important. Why? Because a database typically houses the most important information about your product, customers and business.

A database is at its most basic, a repository for information. With a large software product, you might have multiple databases, but for today we’re going to focus on a single database since most companies start with one. A database could be compared to a fancy spreadsheet.  Instead of tabs like in Microsoft Excel you might have tables and each table contains different bits of information. The way the information in the tables is laid out is called the data model. This is incredibly important as a software product scales because poorly structured data can be a pain in the you know what later on. So it’s not quite as simple as Excel.

The database for your application needs somewhere to live (like AWS or other cloud providers) and an engine that runs it (like Microsoft SQL Server, PostgreSQL, MySQL, Oracle, etc).  For most of our clients, we use AWS and PostgreSQL. In AWS we have a few hosting options, one is that we could spin up an EC2 instance and install PostgreSQL on that instance and then go from there. However, when we do that, we need to worry about making sure it’s highly available (always up), backed up (disaster recovery) and updated (latest version). If that single EC2 instance were to stop working, then we lose access to our data…and with most applications, that’s not a good thing.

That’s why Amazon introduced the Relational Database Service (RDS). RDS is not a specific type of database or database engine. It’s a managed service running in the AWS cloud for databases that is easy to set up, deploy, scale and update a database with the click of the button. No need to worry about high availability or keeping the database versions up to date. RDS will take care of it for you. Need to have everything backed up? No problem, it’s built in to RDS. Instead of building out everything necessary to have a highly available, durable and updated database, it’s already built into RDS.

On the cost front, RDS can look expensive but consider most companies would need to employ a dedicated person to setup and manage the infrastructure. And instead of days to setup a database, it took a few minutes. That’s time to be used elsewhere. RDS has simplified an incredibly complex process and should be considered as part of any scalable software infrastructure.

We’ve covered what a database is, why it’s important and why you should consider RDS as an easy way to setup a highly reliable database running in the AWS cloud. If you have any questions about how this might fit into your project, send us a note.

Amazon recently announced over 100 new cloud services and products at the latest re:Invent conference. While there are tons to be excited about, there are four cloud services that we look forward to using in future projects. Most we’ve had to build from scratch at some point or have had clients ask for them only to be disappointed in the high cost of development. All four services introduce easier ways to integrate machine learning into your cloud based software or mobile application, without significant added cost.

Amazon Personalize

When shopping on Amazon.com, have you noticed the recommendations based on your latest purchases? That’s all created by a recommendation engine that is based on things you like, and things other people like you have purchased too. Amazon is now making similar recommendation models available to developers. Imagine you have an app, like CurEat, that curates local restaurants and lists.  You can now make more personalized recommendations using Amazon Personalize instead of developing something from the ground up.

Amazon Forecast

Remember using spreadsheets to forecast sales for your company, or maybe for a school project? Ok, so maybe not everyone had to do that, but at some point in your career you’ve likely been asked to forecast sales, inventory, or some sort of business or application metric. With Amazon Forecast, you can now feed your data into deep learning algorithms based on the same algorithms Amazon uses for their own business. Amazon says their forecasts are 50% more accurate and are completely automated. Say goodbye to continuous feeding and care of spreadsheets for forecasts. As developers, we can use Amazon Forecast in all sorts of features and components, from predicting product usage to in app features like forecasting production material needs or equipment breakdown in IoT devices.

Amazon Textract

Amazon Textract is like an OCR service except it goes a few steps further and can extract data from fields and tables, and do so very affordably. This is great news for startups or really any company needing to integrate some level of OCR into their product. Imagine quickly building a mobile app that scans old school paper copies of insurance claims, medical records or any paper form. Then taking that data and uploading to a CRM or EHR system, quickly and easily. That’s just the beginning of what’s possible with Textract and there are sure to be more complex, more exciting uses for something that is now incredibly inexpensive and accessible. How inexpensive? Try $1.50 per 1,000 pages for the Document Text API. Read more here.

Amazon Comprehend Medical

Finally, for our medical device and healthcare clients, we’re super excited to see Amazon Comprehend Medical, an expansion of Amazon Comprehend. Amazon Comprehend Medical uses Natural Language Processing to process text in documents and files. Say you have years of medical records that weren’t exactly filed away correctly. Now you can use Amazon Comprehend Medical to process those files and look for patterns. For example, maybe you have an archive of unstructured documents, like physician notes, and you want to extract documents pertaining to a particular medical condition. You can use Amazon Comprehend Medical to look for the medical terminology that coincides with that condition, making it possible to comb through archives in a matter of minutes without manual intervention. It also has the ability to detect Protected Health Information (PHI) which could be used for organizing data or in some cases, avoiding parts of data that may not be necessary for a specific use case.

These are just four of the new services that will be available via AWS in the next few months, and we’re excited to help our clients introduce new features that are now more affordable than ever. If you’d like to hear more about what AWS can offer, contact us or read all the latest announcements from re:Invent here.

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